In general, any image acquisition process is inevitably affected by noise. For example, in the field of X-ray imaging, reducing patient radiation exposure comes at the cost of increasing noise in the captured image. This tradeoff is especially apparent when multiple images are acquired as a time series, such as when monitoring an interventional surgery (e.g., a cardiac catheterization with X-ray fluoroscopy). When monitoring an interventional surgery, a high-quality reconstruction may be required, while providing an efficient approach to reduce varying levels of noise in near real-time. For example, to monitor a surgery in real-time, computed tomography (CT) imaging is often used. CT imaging reconstructs medical images from multiple X-ray projections through the patient at multiple orientations.
As discussed above, due to using ionizing radiation, image acquisition for CT and other x-ray imaging modalities balances a trade-off between the radiation dose and the signal-to-noise ratio of the acquired images. When low-dose radiation is employed, the acquired images may be processed to reduce noise (i.e., denoising). Various image reconstruction and denoising techniques may provide clearer and more interpretable images during the interventional surgery. For example, simple averaging filters are often used for real-time processing, however the filters lead to blurred edges and other details. Advanced algorithms have also been used to reduce signal-dependent noise (e.g., averaging spatial filters (AAS), block-matching and 3D filtering clipped (BM3Dc), etc.) and independent additive noise (e.g., adaptive variational denoising (AV), block-matching and 3D filtering (BM3D), dictionary learning (K-SVD), etc.).
For example, the BM3D algorithm achieves good image denoising results. The BM3D approach is based on providing an improved sparse image representation in a transform-domain. The sparse representation is enhanced by grouping similar 2D image fragments (e.g., image blocks) into 3D data arrays that are filtered using collaborative filtering. The result of the collaborative filtering is a 3D estimate containing jointly filtered grouped image blocks. The filtered blocks are returned to their original positions and averaging is performed over the overlapping blocks. Further extensions and improvements of the BM3D algorithm also may be used, such as the BM4D algorithm using the same approach for 3D image data.
Further, pure data-driven deep learning approaches have been deployed for denoising CT and other imaging modalities. Pure data-driven approaches, however, suffer from a lack of flexibility due to the learnt dependency to the acquisition parameters (e.g., the noise level) and deep learning often becomes too computationally expensive when applied to large 3D volumes in near real-time.